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I have +- 100 kafka topics that can be processed using similar code block.
I am using pyspark 2.2.1
Here is the idea:
TOPIC_LIST = ["topic_a","topic_b"."topic_c"]
stream = {}
for t in TOPIC_LIST:
stream[t] = spark \
.readStream \
.format("kafka") \
.option("kafka.bootstrap.servers",
"kafka-broker1:9092,kafka-broker2:9092") \
.option("subscribe", "{0}".format(t)) \
.load().writeStream \
.trigger(processingTime="10 seconds") \
.format("console") \
.start()
So far, I have these options:
1. do a single spark submit for all topics
2. do multiple spark submit per topics
I am thinking of deploying this using supervisor or upstart so when spark
app is down, I can get notified.
By submitting single spark app I can have more control on server resources
but a little bit hard to monitor if one app is having problem. For multiple
spark submits I am afraid I need to provide +- 100 cpu cores which I doubt
will be fully utilized as some of the topics do not have high througput,
however by creating +- 100 bash scripts maintained using supervisor I can
get alert for each of them.
For monitoring I am considering using this ->
https://argus-sec.com/monitoring-spark-prometheus/
Any recommendation or advices guys?
Thanks
Regards,
Abraham